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Dr Takafumi Nishino Departmental Lecturer in Civil Engineering Fluid Mechanics

Dr

Takafumi Nishino BEng MEng PhD

Senior Research Associate

Biography

Dr Nishino studied mechanical engineering at Kyoto University for his Bachelor’s and Master’s degrees, and obtained his PhD in aerodynamics at the University of Southampton in 2007.

After his PhD, he received a prestigious NASA Postdoctoral Program (NPP) fellowship to spend three years at NASA Ames Research Center in the US. He came back to the UK in 2011 to take another three-year postdoc position in Oxford.

After the long and productive postdoc period, he took a Lectureship in Fluid Mechanics at Cranfield University in 2014. In September 2018, he returned to the University of Oxford to take a Departmental Lectureship in Civil Engineering Fluid Mechanics.  In September 2023, he took up his current role of Senior Research Associate.

More information is available on his personal website.

 

Most Recent Publications

An analytical model of momentum availability for predicting large wind farm power

An analytical model of momentum availability for predicting large wind farm power

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

A data-informed analytic model for turbine power prediction with anisotropic local blockage effects

A data-informed analytic model for turbine power prediction with anisotropic local blockage effects

Time-dependent upper limits to the performance of large wind farms due to mesoscale atmospheric response

Time-dependent upper limits to the performance of large wind farms due to mesoscale atmospheric response

View all

Research Interests

Dr Nishino’s expertise is in the areas of theoretical fluid mechanics and offshore renewable energy, such as wind and tidal-stream energy. During his first postdoc period at NASA, he learnt a variety of advanced modelling techniques for complex turbulent flow simulations. He then extended his research area to offshore renewable energy during his second postdoc period in Oxford. His major achievement during this time was the development of multi-scale flow models to predict the efficiency of a large cluster of tidal-stream turbines. In particular, he mathematically derived a new theoretical upper limit of 79.8% for the efficiency of an isolated cross-stream array of turbines (Nishino & Willden 2012, J. Fluid Mech. 708) compared to the classical "Betz limit" of 59.3% for a single isolated turbine.

More recently, Dr Nishino extended and applied his multi-scale flow analysis to the modelling of large wind farms, partly in collaboration with the UK Met Office. One of his current goals is to develop a novel physics-based approach for fully coupled wind turbine/farm optimisation, by combining his “two-scale momentum theory” for large wind farms (Nishino & Dunstan 2020, J. Fluid Mech. 894) with a regional-scale numerical weather prediction (NWP) model.

Apart from wind and tidal-stream energy, he is also working on a range of fundamental problems in aero/hydrodynamics, such as laminar-to-turbulent transition and separation of the boundary layer, static and dynamic stall of aero/hydrofoils, stability and mixing characteristics of turbulent wake behind a bluff body, and flow-induced vibrations.

Research Projects

Multi-Wind - Addresses key scientific and technical challenges in large-scale wind power generation, aiming to answer what would be the most optimal way to extract the power of wind at a very large scale (with the UK Met Office).

Most Recent Publications

An analytical model of momentum availability for predicting large wind farm power

An analytical model of momentum availability for predicting large wind farm power

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

A data-informed analytic model for turbine power prediction with anisotropic local blockage effects

A data-informed analytic model for turbine power prediction with anisotropic local blockage effects

Time-dependent upper limits to the performance of large wind farms due to mesoscale atmospheric response

Time-dependent upper limits to the performance of large wind farms due to mesoscale atmospheric response

View all

DPhil Opportunities

I am Interested in supervising research students in the areas of theoretical and computational fluid dynamics, wind energy and tidal-stream energy.

Most Recent Publications

An analytical model of momentum availability for predicting large wind farm power

An analytical model of momentum availability for predicting large wind farm power

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

A data-informed analytic model for turbine power prediction with anisotropic local blockage effects

A data-informed analytic model for turbine power prediction with anisotropic local blockage effects

Time-dependent upper limits to the performance of large wind farms due to mesoscale atmospheric response

Time-dependent upper limits to the performance of large wind farms due to mesoscale atmospheric response

View all